We compare to two datasets of videos. One provided by SinGAN-GIF  [1]} and the other by HP-VAE-GAN [2]}. In SinGAN-GIF there are 5 videos with 8 to 15 frames, each of maximal spatial resolution \(168\times 298\) . For each of the 5 input videos, each method generates 6 samples.
In HP-VAE-GAN there are 10 videos each of spatial resolution \(144\times 256\) . For each of the 10 input videos, each method generates 10 samples. HP-VAE-GAN and VGPNN only use the first 13 frames since their methods are limited by runtime and memory. Since learning on small amounts of data is not a goal for the task of diverse generation from a single video, and since our framework can easily learn from much more data, we train our framework on longer sequences of frames from the given input videos.
